Sci‐YIS Fri ‐ 10: Tomographic composition analysis of intact urinary calculi by x‐ray coherent scatter
Bibliographic record
Abstract
Knowledge of urinary stone composition and structure provides important insights in guiding treatment and preventing recurrence. No present method can successfully provide information relating structure and composition of intact stones. We are developing a tomographic technique that uses measures of coherently scattered diagnostic x rays to yield stone composition and structure. Coherent‐scatter (CS) properties depend on molecular structure and are, therefore, sensitive to material composition. For powdered, amorphous or polycrystalline materials with no significant parallel crystal orientation, CS patterns are azimuthally symmetric. In materials with preferred crystallite orientation, such as urinary stones, bright spots appear in their CS patterns. This may compromise a composition analysis based on comparing CS measurements from urinary calculi to a library of CS signatures from powdered chemicals. We show that a tomographic reconstruction of CS measurements (CSCT) effectively eliminates bright spots and yields CS patterns equivalent to powders. This allows for direct comparison with a powdered chemical reference library and provides more accurate material identification. Validation was achieved using an aluminium rod phantom, which exhibits bright spots much like calculi. CSCT composition analysis was performed on intact stones deemed chemically pure by infrared spectroscopy. Computed tomographic reconstruction of CS signals allowed the generation of composition maps, showing the distribution of components. These images provide strong evidence that current laboratory techniques risk missing critical stone components in their analysis due to inadequate sampling. This supports the development of CS analysis as a stone analysis technique both in the laboratory and possibly in situ.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".